Event Report: Data Driven Last Mile

19-02-2019 – Evoluon

On Tuesday the 19th of February, Data2Move community members gathered at the Evoluon in Eindhoven for a brand new Data2Move event. After events on Collaboration and Data Driven Inventory, this event revolved around the topic of transportation. Themed Data Driven Last Mile,  the objective was to show the participants how different variables affect transportation and in particular, how data can help enhance transport planning decisions.

After a delicious lunch, Prof. Luuk Veelenturf opened the event by introducing the programme of the day. However, there was no time to sit back and relax for too long because the first part of the scheduled workshop called everyone into action. The goal of this workshop? To show the participants how they can use data to increase the quality of their transportation decisions.

Workshop Part I: Travel Time Data Analysis

Before the workshop started Veelenturf shared the results of the guessing exercise that was part of the registration procedure for the event. Every participant had to make an educated guess on the registration form of the event regarding the average speed of a truck on the Dutch road during its delivery route with intervals of two hours. The winner, Mr. Ingmar Scholten (CTAC), had an impressive score of 59%. Could the use of the data driven approach beat this excellent score…?

At the start of the workshop Veelenturf briefly addressed the planning of delivery routes (routing) and how some routes may take longer depending on factors such as length, weather, time of the day and day of the week. After this, the participants were divided into small teams of four to five people. Based upon a large dataset of truck delivery routes (including speed, time and distance) the different teams had to predict the average speed within a two hour time window starting from 6:00 until 18:00 and a time-window for delivery that would best match those average speeds. This input was then compared to the data of approximately a thousand random pre-picked routes. Each prediction was assessed by calculating the percentage of trips that actually arrived within the time window that a company gave to its customers based on forecasted speeds and the time window setting.

All of the teams were supervised by a BSc/MSc/Phd/PDEng student with access to the dataset. It was up to the partners to discuss and think of ways to filter the data and come to average speeds and a suitable time window for delivery. The students were equipped with a pre-programmed tool to aid the process. Most teams came to suitable average speeds. However, the winning team of this first part of the workshop did not succeed in beating Mr. Scholtens’ score…..yet. The question remained whether this score would be beaten at all in the second part of the workshop.

Keynote by Stefan Minner (TU Munich) on Routing with Uncertain Travel and Service Times

When one was under the impression that after the exciting first part of the workshop they could finally sit back and let someone else do the work, they were most certainly wrong. The keynote by Prof. Stefan Minner was very engaging but challenging. Minner talked about a Data-Driven approach for routing of delivery services under uncertain travel and service times. Most models use deterministic travel and service times but according to Minner this produces incomplete results in practice. Minner pointed out how machine learning can be applied to logistics and transportation. He also addressed the difference between predictive analytics (sequential approach) and prescriptive analytics (integrated approach) and pointed out that a significant increase in forecasting accuracy does not always necessarily leads to a significant increase in performance. In conclusion Minner explained the data driven approach to the vehicle routing problem with time windows and showed some computational results and numerical tests of this approach. After this, it was time for a well-deserved coffee break and some network opportunities.

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Different kind of analyses on data

Workshop Part II: One last time to improve your estimates

After the break, the results of the first workshop were highlighted in the second part of the workshop. Veelenturf shared some of the results of a study done by one of his students on the same dataset. This student had come to 18 different speed profiles based upon differences in distance, urbanisation and day of the week. By using these profiles, the student managed to predict travel speeds that accounted for almost 90% of routes arriving within the specified time window. Based on this prediction this student also managed to optimise the truck planning using software developed by TU/e.

Inspired by this example, the teams then got a chance to improve their scores by making different speed profiles based upon distance. So every team had to produce speed profiles and a time window, but now they also had to choose three different distance categories for different speed profiles.

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Different Speed Profiles

Compared to the initial guesswork and the results of the first part of the workshop, each team showed a large increase in their on-time scores. This shows that further exploration of the data and the determination of more profiles by setting parameters was beneficial to the on-time scores. A powerful conclusion to illustrate the importance of good data analytics and critical thinking.

After all this data-crunching it was finally time to announce the winning team. Every team-member received a small device that enables you to track your own speed throughout the day. We will have to find out at the next event if the results of their personal speed tracking are just as impressive as their prediction accuracy.

Next steps

The theme featured in this Data2Move event was transportation. The topic of the upcoming Data2Move event in May is Customer Sensing and Responding. This next event will feature a number of on-going student projects (Bachelor and Master). Students will share the valuable insights they have discovered and there is room to give them your input as a professional. As Customer Sensing and Responding is the last charter, this means that for the event after May, we, as a community, can go in any direction we want. Please do not hesitate to share your ideas about possible topics.

Data2Move Research Stories: Automated Store Ordering to improve a supermarket’s inventory management

Where supply chain management and data science meet, interesting questions arise. In our Data2Move Research Stories, you’ll find out how students have managed to answer them. This time: Bob van Beuningen’s master thesis on Automated Store Ordering versus Manual Store Ordering at Jumbo Supermarkten.

Ever since the Dutch retail market entered a fierce price war in 2003, retailers have continuously been looking for ways to save costs while maintaining the high service level that is demanded by customers.

That’s where, for instance, an Automated Store Ordering system comes in. This can reduce food waste, reduce stock outs, and can save employees a significant amount of work.

Where it started

Every one of Jumbo’s supermarkets relies on such an ASO system to predict the amount of goods that should be in stock on any given day. A challenge, however, is in the fact that 9% of the generated orders are manually adapted by store managers.

Jumbo wanted to find out why they make these adjustments. By finding out, the system could be changed in order to create a so-called hands-off policy, meaning adjustments would never be necessary.

Bob focuses on this in his thesis – and also delivers recommendations for Jumbo to judge the ‘correctness’ of the adjusted orders (i.e. whether the orders add more value or whether the adjustment only costs more money).

Findings

Bob conducted interviews and performed a logistic regression analysis. Three main reasons were found that actively cause managers to adjust the orders:

  • The product is on promotion
  • The product is on second placing (i.e. store managers have allocated extra shelf space, typically at the head of an aisle)
  • The inventory in the ASO system was incorrect

Additionally, it was tested if these order adjustments added value, meaning they were good for the company. First it turned out 75% of the adjustments meant a bigger order and 25% meant a smaller one. Results were that only 15% of ‘upwards adaptations’ added value and 65% of downward adaptations added value. What seemed to contribute to the latter was the question whether or not a product was perishable. For instance, downward adjustments in perishable products are more likely to add value.

“Store managers are more likely to add value for perishable products than for non-perishable products,” Bob wrote.

Further advice

In order to get better results, Bob advises Jumbo to change some of their Key Performance Indicators: “It is recommended to use the KPIs ‘process trustworthiness’, ‘added value of order adaptions’, and ‘order acceptance’ to move to a hands-off situation. It is important to use these KPIs to find out whether a specific store is able to move to a hands-off policy or not.”

“If an adaptation was correct, and thus added value, then the system should be able to recognize this and make the adaptation itself in the future. By means of these KPIs, Jumbo should be able to get more insights in what needs to be changed in the system in order to achieve this goal.”

How do companies successfully set up partnerships?

What makes partnerships work? During ‘Collaboration: Discovering the Potential’, a Data2Move community event, prof. dr. Ard-Pieter de Man (Vrije Universiteit Amsterdam) shared valuable insights from extensive research. A certain attitude is important: “You don’t want to be in control, you want to be up to speed. To do that, you need to work together.”

Both researcher and consultant, professor De Man is an expert on partnerships between organizations. He is highly interested in organizations’ capability to change – and how partnerships can help make that happen.

Partnerships are on the rise, said prof. De Man while discussing current trends. Companies in the IT and pharmaceutical industries are taking the lead. “And not only that: they also make an effort to find out how to manage these collaborations. At the beginning of our studies, the companies we tracked used 11 tools to manage their collaborations – evaluations, legal aspects, etcetera. By the end, ten years later, they had 30 tools.”

One of the other trends: there are more and more multi-company partnerships. “Six, seven, sometimes even ten companies work together.”

3 core elements of success

Many partnerships fail, prof. De Man said, because of a mismatch between strategies or cultures, or because of a lack of trust. But then what makes partnerships successful? He shared three core elements of success:

  • Structure
  • Relationship
  • Collaborative capability

You can properly take care of these by answering certain questions. For instance, in the case of structure: Who talks to whom? Which goals do we discuss? How are we going to share costs and revenue? Or in terms of relationship: How do we build trust? Is everyone committed? And collaborative capability: Are we willing to share knowledge? Do we have the right tools? Are we able to collaborate?

Everyone benefits: norms for collaboration

De Man emphasizes the importance of the relational aspect. Introducing his list of norms for successful collaborative behavior, deduced from research, he starts with empathy. Can you understand how the collaboration affects your partner(s)?

This is related to mutuality. “Your company benefits, and so does your partner,” De Man explained. “This is still a problem for many companies.”

In comes flexibility. Markets, needs, goals: circumstances may change. “Are you willing to evolve with them? Or do you want to stick to the contract?”

Other norms include commitment, a willingness to solve conflicts, and a strategic outlook: “If you can actually look ahead by two or three years, all partners involved can reap tremendous benefits – but all too often the focus is on the short term.”

To show how these success factors and norms translate to daily behavior, De Man shared the Abbott-Reata Behavioral Principles, including…

  • try a talk before you e-mail
  • celebrate achievements together
  • make it a habit to share information

Increasingly interdependent

Discussing examples from Air France and KLM (initially offering their customers more destinations; eventually learning from each other) and John Deere and Kespry (drones gathering topographic data; tractor drivers benefiting from data while planting), professor De Man showed how companies are becoming increasingly interdependent. And partnerships like these are just the beginning. All together, they help shape big ecosystems, where the fate of individual companies becomes intertwined.

“We still think of competition as something between companies, but it’s becoming something between ecosystems,” said De Man. “In a data-driven economy, your success depends on your partner ecosystem. An ecosystem with the different parts supporting and strengthening each other.”

Getting up to speed

De Man makes it clear: managing diverse partnerships is becoming a competitive advantage – and the human element becomes even more important than it already was. This was an idea that resonated throughout our event. Asking our community members which take-away they found especially noteworthy, many of them responded along these lines:

  • “It’s not about technology, it’s about empowering people.”
  • “Collaborations start with relationships.”
  • “It’s the people who collaborate, not the companies.”

For companies who are hesitant to start forming partnerships, professor De Man provides a solid reminder: “With today’s rapid developments, you don’t want to be in control – you want to be up to speed. To do that, you need to work together.”

Become a part of the Data2Move community and join us for our next event in February.

More on professor De Man’s work can be found on his Vrije Universiteit Amsterdam profile.

Data2Move Research Stories: improving demand planning for an international manufacturer

Where supply chain management and data science meet, and where theory and practice meet, that’s where Data2Move meets. At intersections like these, interesting questions arise. In our Data2Move Research Stories, you’ll find out how students have answered them.

This time, we look into Nazli Akgül’s master thesis: a study of the relationship between a company’s demand planning process and its supply chain performance.

Where it started

This international manufacturer of pharmaceutical products aimed to improve its performance regarding inventory management and planning – and save costs thanks to this improvement.

Where did this objective come from? Well: the company noticed its demand planning took to much time. As a consequence, their decision-making process was often hurried, and led to suboptimal outcomes. This problem was found throughout the company across multiple countries. It affected the inventory, which was often inaccurate, and raised costs unnecessarily.

Akgül took to the problem with an in-depth analysis of the company’s planning activities and the way they related to inventory levels and performance. She found that some activities held back performance – and others turned out to be more important than initially thought.

Findings and advice

By using simulation techniques, Akgül investigated possible changes to see what effect they would have. Of course, reality may differ from the simulations, but they generally prove to be valuable. It was concluded that the company should try to reduce reporting tasks for planners and, instead, allocate more time for backorder analysis and demand consensus meetings with supply chain partners.

By doing so, predicting demand would no longer be an issue, the company’s inventory would be managed accurately from now on, and performance would increase significantly.

Furthermore, thorough data analyses showed that demand planning performance significantly decreased when planners had to spend time on assuring the data are of good quality. Therefore, it is strongly recommended that the manufacturer adopts a more accurate data system.

The new approach Akgül recommends, including the new data system to support it, would require change on a large scale. An investment at first, which makes for a major time-saver later on.

Professor Arjan van Weele on Supply Network Collaboration, part 2: ask your network what keeps them up at night

On October 30th, it’s time for Data2Move’s next gathering – Collaboration: Discovering the Potential. Arjan van Weele, professor of Purchasing and Supply Chain Management, will co-host the workshop. But first, in part 2 of this interview, he shares some more thoughts on Supply Network Collaboration: “You achieve more when everyone joins forces.”

Humanity has a great capacity to adapt to new circumstances. However, in our interconnected world, you can clearly see there is a limit to this capacity. It’s important to take into account how much change people and organizations can accept and digest.”

It’s the same when you’re talking about supply network collaboration. It’s a learning process with ups and downs. There are companies who do it extremely well – here in the Eindhoven area, for instance – but for most companies there’s a long way to go.”

Get into it for the long term

Look at retail, for instance. A while ago, I read about a German retail chain taking 450 Unilever products off the shelves because they couldn’t agree on a price increase. The customers seem to come in second, they have nothing to say about this. Why not look at the situation together, find out how to take costs out of the chain, and see what both parties want to make as a margin. This kind of powerplay leads nowhere.”

Based on science, we know that collaboration brings much more than arm wrestling. Supply network collaboration will not result in better financial results today, but it will do so tomorrow. If we only focus on the short term, meaning quarterly results, this will generate opportunistic behavior in your relationship with supply partners and customers. That’s a hassle you need to prevent. In each and every supply chain, there is so much waste to take out, so many costs to be avoided.”

A meeting of the minds

Companies already depend on their suppliers in terms of product and service quality. Now, they also depend on them in terms of innovation. Getting the best innovations from suppliers means you need to invest in your relationships. Innovation is all about people. Many companies agree they need to stimulate joint knowledge sharing and innovation. But that requires a new approach to collaboration. It takes a lot of trust – both personal trust and institutional trust. That illustrates the importance of the Data2Move event’s theme.”

We should realize that you get the best solutions when you bring all relevant companies and specialists together. Supply network collaboration always requires a meeting of minds.”

The first step towards network collaboration

What would be a good first step towards collaboration in networks? Professor Van Weele: “Ask your network which questions they currently want answered, what it is that bothers them. For instance, managing their chains: what keeps them awake? They might say: ‘You know what we think is difficult? Predicting demand. Especially discontinuities.’ In our case, we would say: well, we’ve developed these forecasting models, do you think they’d work for you?”

Or perhaps your network might say: ‘You know, we suffer from stock obsolescence.’ Perhaps because their products’ shelf lives are getting shorter – while a purchaser enthusiastically bought a lot of them, in order to get a discount. How should they make decisions from now on? Another example of a question: ‘In our business scarcity is an issue. We have to wait an entire year for certain components for our Printed Circuit Boards, but we need to have them done in three months.”

I hope we get to discuss questions like these during our event, so we can start doing something about them. That’s where our community’s enormous amount of knowledge, experience and creativity comes in.”

Join us next week in Eindhoven: sign up now.

Also check out part 1 of our interview with professor Arjan van Weele: ‘Companies unaware of how dependent they are

Professor Arjan van Weele on Supply Network Collaboration: companies unaware of how dependent they are

On October 30th, the Data2Move community will meet again to explore Supply Network Collaboration. In this two-part article, Arjan van Weele, professor of Purchasing and Supply Chain Management, shines some light on this thought-provoking topic: “Companies can no longer ignore the fact that good collaboration is vital.”

Many of us are not aware of this, but we are all completely dependent on others. For our survival, for peace, for prosperity. Especially now that the world is becoming more and more interconnected. We have to learn how to collaborate in networks,” says Professor Van Weele.

Companies often don’t realize how dependent they are. Until there’s a flood in Thailand, leaving three factories under water that produce semiconductors, required by companies here to make Printed Circuit Boards – who then send people home, because the process grinds to a halt.”

Competition is becoming a myth

Based on our research, we can now see that competition is becoming a myth,” he states. “One supermarket competing with another… That’s not really what’s going on. It’s these companies’ supply chains that are competing. Look at the automotive world: it’s the suppliers of Volkswagen that define the quality of their cars.”

By now, we have realized that companies are so dependent on their supply chain partners, for the quality of their products and services, as well as their reputation, that they can’t ignore the fact that good collaboration is vital. It is ASML’s network that enables the company to realize the innovations they need in order to survive. They have to innovate every 18 months. Moore’s Law. You can’t make that happen with a loosely coupled system. Supply Network Collaboration is about companies, their suppliers, their customers and knowledge partners, all joining forces in a network.”

Different approaches to Supply Network Collaboration

The interesting thing is that you can approach it from different angles. From the innovation side, or from the operations side. From the logistics side, of course: how do we optimize the flow of goods? From the procurement side – how do the various parties deal with each other in terms of contracts? Or what about the IT side: how do we exchange data, how do we make sure all partners are connected?”

And, last but certainly not least, there is the human side of enterprise: how can we get people from different companies in different sectors, all with different company cultures, to work together without any obstacles?”

It’s far from easy,” says Professor Van Weele, and he smiles: “Well, that’s the theme of the day!”

Next week: part 2, on companies who are doing this well, and the first steps you can take on the way to Supply Network Collaboration. Data2Move members: be sure join us on October 30th!

Data2Move event report: Data-Driven Inventory

June 14, 2018 – hosted by community partner SAP in s’Hertogenbosch

During the interviews with our community members, the Data-Driven Inventory charter unequivocally raised most interest. The event was thus focused on sharing the first research insights on this common theme.

Opening with SAP and Sarah Gelper on the Data2Move Community

After a short welcome by Jan-Theodoor Wiltschek from SAP, Sarah Gelper from TU/e caught up on Data2Move’s progress: from the brainstorming sessions during the kick-off in September 2017, via the Data Ambition Session of February, to presenting the first results today and towards defining joint projects as a next step.

The First Results! – Insightful reports from 3 research projects

Time (in)efficiencies in demand planning by Nazli Akgül (Valeant)

How can demand planners use their time more effectively to obtain better forecasts and reduce backorder value? Using the Lean Six Sigma methodology, Nazli Akgül found that while time spent on forecasting increases accuracy, time spent on reports, meetings and data quality issues decreases accuracy. Her study also showed that time spent on backorder analysis and meetings with product managers decreases backorder value, while time spent on data quality issues increases backorder value. These results emphasize the need for integrated data quality systems.

Advised versus actual orders by Bob van Beuningen (Jumbo)

Why do store managers order different quantities than suggested by the order advise system? Bob van Beuningen found that the four most important reasons for such deviations are: second placings, changing weather, improving the store view and inventory collection. While store managers thus have good reasons to make order adjustments, these adjustments are often incorrect – especially when the actual order exceeds the adviced order. As a next step, he will investigate whether there are differences between product categories.

Starting with demand forecasting by Bas Buijse (TKT)

Which type of time series model best predicts demand for laundry? Bas Buijse conducted an extensive forecast method comparison, including external variables such as the mean temperature, humidity, sunshine duration and the GoogleTrends results of the search term ‘hotel’. His analysis shows that forecasts which take external variables into account can be very powerful and can help to better assign resources.

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Bas Buijse explaining the results of his research

Masterclass Mashup – Deep dive into one of the three key concepts in Data-Driven Inventory

Big Data to Mitigate the Bullwhip Effect by Zümbüt Atan (TU/e)

Zümbül Atan gave a masterclass on a very important phenomenon in inventory management: the bullwhip effect. There are some structural causes of the bullwhip effect, but there are also behavioral reasons caused by human decision making. She explained these causes by showing the results of a real-life experiment: the beer game. Although big data cannot entirely eliminate the bullwhip effect, integrating large and various sets of data in real time, sharing data and interacting in a collaborative manner can help to reduce the bullwhip effect.

Forecasting: Algorithm vs Human by Sarah Gelper (TU/e)

Sarah Gelper gave a masterclass on forecasting, focusing on the difference between algorithmic forecasting models and forecasting done by experts. Using her own research, other research and industry experience, she concluded that neither type of forecasting is better. Rather, a combination of algorithmic models and human expertise is virtually always the better option regarding demand forecasting.

Inventory and Transportation by Luuk Veelenturf (TU/e)

Luuk Veelenturf’s masterclass focused on transportation. The importance of online shopping over regular shopping is increasing and the role of transportation in online shopping plays crucial role. He highlighted the need for efficiency in transportation by increasing volume utilization and hit rates. In order to achieve this, the role of data science in transportation is very important. He also mentioned different kinds of data which can be used in transportation such as volume, travel & services time and delivery location. He concluded his presentation with the privacy issues in data that can be used in transportation for delivery optimization model.

Keynote Data-Driven Inventory in practice by Dori van Hulst (Pipple)

Dori van Hulst from Pipple took us on a treasure hunting trip. Just like on an actual treasure hunting trip, multiple elements are required for a successful data-science project: a clear goal, a crew, the data you want to use, knowledge and insights to gain from the data, deciding if the treasure is in line with company goals and data science techniques which are needed to find the treasure. All attendants defined these elements for their own treasure hunt, enabling them to take the first next steps.

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The community filling out their own treasure hunt list

Partners on Stage – Meet three community partners and their practice

CTAC has facilitated the new online platform called ‘Movement’. The community can use this platform to share updates on research projects, discuss ideas to benefit from each other’s expertise and keep the knowledge flowing in between meet-ups. Jordi Peters pointed out the importance of working together and their willingness to collaborate.

De Persgroep is a new member of the Data2Move community. Gerda van der Poel explained the challenges De Persgroep currently faces in light of the evolving landscape of news consumption, and the way their business is changing. As exploring data can help them to set up a new logistics strategy, De Persgroep is excited to improve their distribution with the help of Data2Move.

Siel Vroman introduced the Data2Move community member Pro Alliance. She explained the benefits of Zuckerberging your supply chains, thereby introducing the theme of next event: Supply Network Collaboration.

 

Logistics researchers present Data Ambition Matrix

How can you as a company make use of Big Data the upcoming years to work more effectively  in a supply chain? And where are you now? These questions can now be answered using the Data Ambition Matrix, developed by research community Data2Move.

Data2Move is the leading community on the interface of Big Data, Internet of Things, logistics and supply chain management. Academics from Eindhoven University of Technology, Tilburg University and the Jheronimus Academy for Data Science work together with companies like Den Hartogh, Jumbo, Nabuurs, SAP and Philips to find new solutions.

Formulate ambitions

“The community focusses on the usage of data to improve your organization and your supply chain,” says Paul Grefen, member of Data2Move and professor at the School of Industrial Engineering at Eindhoven University of Technology. Data2Move kicked-off in September 2017 and now presents their first tool: the Data Ambition Matrix. Inspired by the industry, Grefen tells: “Based on our collaborations with industry we saw that many companies do understand the importance of data, but have difficulties to formulate focused ambitions and to determine how to realize these ambitions.“

The Data Ambition Matrix is a framework that consists of two dimensions. Grefen: “A horizontal dimension that shows the level of integration of data, and a vertical dimension that shows the realization of this integration. We ask companies to plot themselves three times in the matrix: where are they now, where do they want to be in two years, and in five years?”

Looking beyond silo’s

“At the moment we see that many companies stay stuck at using data in silo’s within their own firm. The Procurement department collects data, the Manufacturing department also collects data, but they are not connected to each other. Then, it could be the case, for example, that the Procurement department purchases too much material because it is unclear what the Manufacturing department needs. The Data Ambition Matrix creates awareness of the opportunities that exist. On the realization axis, you can move forward to the usage of real-time data. On the integration level you can work to market-data-integration.”
“Traditionally materials, finance and workforce were the pillars of a successfull firm. Now data is the unneglible fourth pillar,” states Grefen. “In the very near future supply chains will be dynamically shaped by data. If you don’t respond to this as a company, you will lag behind.”

Watch the full video about the Data Ambition Matrix

Introducing Movement

Movement is our new platform, where the latest projects and updates are shared. As a member of Data2Move you are eligible to join this platform. The platform provides detailed information on each ongoing project and has contact details for both partners and students participating within Data2Move.

The platform also includes more detailed information on each charter and possible project directions.

Go to movement

Inquire about partnership